Fixes for numpy 2 (#124)

* np.float_ -> float

* Build with numpy 2+

* Try supporting python 3.8

ndsplines/_npy_bspl.py

@@ -10,11 +10,11 @@ def find_interval(t, k, xvals, extrapolate=False, workspace=None):
 
     Parameters
     ----------
-    t : ndarray, shape=(n+k+1,) dtype=np.float_
+    t : ndarray, shape=(n+k+1,) dtype=float
         knots
     k : int
         degree of B-spline
-    xvals : ndarray, shape=(s,) dtype=np.float_
+    xvals : ndarray, shape=(s,) dtype=float
         values to find the interval for
     extrapolate : bool, optional
         whether to return the last or the first interval if xval
@@ -105,14 +105,14 @@ def evaluate_spline(t, k, xvals, nu, extrapolate,
 
     basis_workspace = basis_workspace.T
     if (not isinstance(basis_workspace, np.ndarray) or 
-            (basis_workspace.dtype != np.float_) or
+            (basis_workspace.dtype != float) or
             (basis_workspace.shape[0] < 2*k+2) or
             (basis_workspace.shape[1] < s)):
         raise ValueError("basis_workspace has invalid shape or dtype")
 
     u = basis_workspace[:k+1,:s]
     w = basis_workspace[k+1:2*k+2,:s]
-    bounds = np.empty((2,s), dtype=np.float_)
+    bounds = np.empty((2,s), dtype=float)
 
     u[0,...] = 1.0
     for j in range(1, k-nu+1):

ndsplines/ndsplines.py

@@ -26,9 +26,9 @@ class NDSpline(object):
 
     Parameters
     ----------
-    knots : list of ndarrays, shapes=[n_0+degrees[i]+1, ..., n_ndim+degrees[-1]+1], dtype=np.float_
+    knots : list of ndarrays, shapes=[n_0+degrees[i]+1, ..., n_ndim+degrees[-1]+1], dtype=float
         List of knots in each dimension, :math:`[t_1, t_2, \ldots, t_N]`.
-    coefficients : ndarray, shape=(n_1, n_2, ..., n_xdim) + yshape, dtype=np.float_
+    coefficients : ndarray, shape=(n_1, n_2, ..., n_xdim) + yshape, dtype=float
         N-D array of coefficients, :math:`c_{i_1, ..., i_N}`.
     degrees : ndarray, shape=(xdim,), dtype=np.int_
         Array of the degree of each dimension, :math:`[k_1, k_2, \ldots, k_N]`.
@@ -39,11 +39,11 @@ class NDSpline(object):
 
     Attributes
     ----------
-    knots : list of ndarrays, shapes=[n_0+degrees[i]+1, ..., n_ndim+degrees[-1]+1], dtype=np.float_
+    knots : list of ndarrays, shapes=[n_0+degrees[i]+1, ..., n_ndim+degrees[-1]+1], dtype=float
         List of knots in each dimension, :math:`[t_1, t_2, \ldots, t_N]`.
     xdim : int
         Dimension of spline input space.
-    coefficients : ndarray, shape=(n_1, n_2, ..., n_xdim, ydim), dtype=np.float_
+    coefficients : ndarray, shape=(n_1, n_2, ..., n_xdim, ydim), dtype=float
         N-D array of coefficients, :math:`c_{i_1, ..., i_N}`.
     ydim : int
         Dimension of spline output space.
@@ -123,7 +123,7 @@ def allocate_workspace_arrays(self, num_points):
                 self.xdim,
                 self.current_max_num_points,
                 2*self.max_degree+2,
-            ), dtype=np.float_)
+            ), dtype=float)
             self.interval_workspace = np.empty((self.xdim, self.current_max_num_points, ), dtype=np.intc)
             self.coefficient_selector = np.empty((self.current_max_num_points,) + self.coefficient_shape_base, dtype=np.intc)
 
@@ -135,7 +135,7 @@ def compute_basis_coefficient_selector(self, x, nus=0):
 
         Parameters
         ----------
-        x : ndarray, shape=(s, self.xdim,) dtype=np.float_
+        x : ndarray, shape=(s, self.xdim,) dtype=float
             Points at which to evaluate the spline.
         nus : int or ndarray, shape=(self.xdim,) dtype=np.int_
             Order of derivatives for each dimension to evaluate. Optional, 
@@ -185,15 +185,15 @@ def __call__(self, x, nus=0):
 
         Parameters
         ----------
-        x : ndarray, shape=(..., self.xdim) dtype=np.float_
+        x : ndarray, shape=(..., self.xdim) dtype=float
             Points at which to evaluate the spline. 
         nus : ndarray, broadcastable to shape=(self.xdim,) dtype=np.int_
             Order of derivatives for each dimension to evaluate. Optional, 
             default is 0.
 
         Returns
         -------
-        y : ndarray, shape=(..., self.yshape) dtype=np.float_
+        y : ndarray, shape=(..., self.yshape) dtype=float
             Value of N-dimensional B-spline at x.
 
         """
@@ -209,7 +209,7 @@ def __call__(self, x, nus=0):
 
         x_shape, x_ndim = x.shape, x.ndim
         # need to double transpose so slices of all `i`th dim coords are c-contiguous
-        x = np.ascontiguousarray(x.reshape((-1, self.xdim)).T, dtype=np.float_).T
+        x = np.ascontiguousarray(x.reshape((-1, self.xdim)).T, dtype=float).T
         num_points = x.shape[0]
 
         if isinstance(nus, np.ndarray):
@@ -515,7 +515,7 @@ def _not_a_knot(x, k, left=True, right=True):
 
     Parameters
     ----------
-    x : ndarray, shape=(n,), dtype=np.float_
+    x : ndarray, shape=(n,), dtype=float
         Knot array to perform the not-a-knot procedure on
     k : int
         Degree of desired spline
@@ -699,7 +699,7 @@ def make_interp_spline(x, y, degrees=3, bcs=(-1,0)):
 
         # set up the LHS: the collocation matrix + derivatives at boundaries
         kl = ku = k
-        ab = np.zeros((2*kl + ku + 1, nt), dtype=np.float_, order='F')
+        ab = np.zeros((2*kl + ku + 1, nt), dtype=float, order='F')
         _sci_bspl._colloc(x_slice, t, k, ab, offset=nleft)
         if nleft > 0:
             _sci_bspl._handle_lhs_derivatives(t, k, x_slice[0], ab, kl, ku, deriv_l_ords)

pyproject.toml

@@ -21,7 +21,11 @@ classifiers=[
 ]
 requires-python = ">=3.8"
 
-dependencies = ["numpy", "scipy"]
+dependencies = [
+    "numpy<2; python_version<='3.8'",
+    "numpy; python_version>'3.8'",
+    "scipy",
+]
 
 [project.optional-dependencies]
 test = ["pytest", "pandas"]
@@ -34,8 +38,8 @@ requires = [
     "setuptools>=45",
     "setuptools_scm[toml]",
     "cython",
-    "oldest-supported-numpy; python_version<'3.12'",
-    "numpy>=1.26.0; python_version>='3.12'",
+    "oldest-supported-numpy; python_version<='3.8'",
+    "numpy>=2; python_version>'3.8'",
 ]
 
 [tool.setuptools]